Heuristic Task Consolidation Techniques for Energy Efficient Cloud Computing

2015 ◽  
pp. 238-260 ◽  
Author(s):  
Dilip Kumar ◽  
Bibhudatta Sahoo ◽  
Tarni Mandal
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Centers ◽  
Optimal Solution ◽  
Solution Space ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Cloud Infrastructure ◽  
Two Phase ◽  
Phase Selection

The energy consumption in the cloud is proportional to the resource utilization and data centers are almost the world's highest consumers of electricity. The complexity of the resource allocation problem increases with the size of cloud infrastructure and becomes difficult to solve effectively. The exponential solution space for the resource allocation problem can be searched using heuristic techniques to obtain a sub-optimal solution at the acceptable time. This chapter presents the resource allocation problem in cloud computing as a linear programming problem, with the objective to minimize energy consumed in computation. This resource allocation problem has been treated using heuristic approaches. In particular, we have used two phase selection algorithm ‘FcfsRand', ‘FcfsRr', ‘FcfsMin', ‘FcfsMax', ‘MinMin', ‘MedianMin', ‘MaxMin', ‘MinMax', ‘MedianMax', and ‘MaxMax'. The simulation results indicate in the favor of MaxMax.

Heuristic Task Consolidation Techniques for Energy Efficient Cloud Computing

2016 ◽  
pp. 760-782 ◽  
Author(s):  
Dilip Kumar ◽  
Bibhudatta Sahoo ◽  
Tarni Mandal
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Centers ◽  
Optimal Solution ◽  
Solution Space ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Cloud Infrastructure ◽  
Two Phase ◽  
Phase Selection

The energy consumption in the cloud is proportional to the resource utilization and data centers are almost the world's highest consumers of electricity. The complexity of the resource allocation problem increases with the size of cloud infrastructure and becomes difficult to solve effectively. The exponential solution space for the resource allocation problem can be searched using heuristic techniques to obtain a sub-optimal solution at the acceptable time. This chapter presents the resource allocation problem in cloud computing as a linear programming problem, with the objective to minimize energy consumed in computation. This resource allocation problem has been treated using heuristic approaches. In particular, we have used two phase selection algorithm ‘FcfsRand', ‘FcfsRr', ‘FcfsMin', ‘FcfsMax', ‘MinMin', ‘MedianMin', ‘MaxMin', ‘MinMax', ‘MedianMax', and ‘MaxMax'. The simulation results indicate in the favor of MaxMax.

Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm

2016 ◽  
Vol 12 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Mohammed Ibrahim ◽  
Haider AlSabbagh
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Power Allocation ◽  
Communication Systems ◽  
Optimal Solution ◽  
Solution Space ◽  
Allocation Problem ◽  
Base Stations ◽  
Resource Allocation Problem ◽  
Effective Parameters

A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.

A Multi-Objective Decision and Analysis Approach for the Berth Scheduling Problem

2012 ◽  
pp. 1-20
Author(s):  
Mihalis M. Golias ◽  
Maria Boilé ◽  
Sotirios Theofanis ◽  
Heidi A. Taboada
Keyword(s):  
Resource Allocation ◽  
Container Terminal ◽  
Solution Space ◽  
Allocation Problem ◽  
Container Terminals ◽  
Analysis Approach ◽  
Scheduling Problem ◽  
Resource Allocation Problem ◽  
Multi Objective ◽  
Berth Scheduling

Berth scheduling can be described as the resource allocation problem of berth space to vessels in a container terminal. When defining the allocation of berths to vessels container terminal operators set several objectives which ideally need to be optimized simultaneously. These multiple objectives are often non-commensurable and gaining an improvement on one objective often causes degrading performance on the other objectives. In this paper, the authors present the application of a multi-objective decision and analysis approach to the berth scheduling problem, a resource allocation problem at container terminals. The proposed approach allows the port operator to efficiently select a subset of solutions over the entire solution space of berth schedules when multiple and conflicting objectives are involved. Results from extensive computational examples using real-world data show that the proposed approach is able to construct and select efficient berth schedules, is consistent, and can be used with confidence.

scholarly journals Heuristics for Efficient Resource Allocation in Cloud Computing

2019 ◽  
pp. 01-21 ◽  
Author(s):  
Su Seon Yang ◽  
Nong Ye
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Optimization Problem ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Resource Allocation Problem ◽  
Optimal Solutions ◽  
Problem Types ◽  
Efficient Resource ◽  
Resource Service

The resource allocation in cloud computing determines the allocation of computer and network resources of service providers to service requests of users for meeting user service requirements. It is not scalable to solve the resource allocation problem as an optimization problem to obtain the optimal solution in real time. This paper presents the development and testing of heuristics for the efficient resource allocation to obtain near-optimal solutions in a scalable manner. We first define the resource allocation problem as a Mixed Integer rogramming (MIP) optimization problem and obtain the optimal solutions for various resource-service problem types. Based on the analysis of the optimal solutions, we design heuristics for the efficient resource allocation. Then we evaluate the performance of the resource allocation heuristics using various resource-service problem types and different numbers of service requests and resources. The results show the comparable performance of the heuristics to the optimal solutions. The resource allocation heuristics also demonstrate the better computational efficiency and thus scalability than solving the MIP problems to obtain the optimal solutions. Keywords: Resource allocation; Clouds computing; Heuristics; Mixed integer programming

A Multi-Objective Decision and Analysis Approach for the Berth Scheduling Problem

2010 ◽  
Vol 1 (1) ◽  
pp. 54-73 ◽  
Author(s):  
Mihalis M. Golias ◽  
Maria Boilé ◽  
Sotirios Theofanis ◽  
Heidi A. Taboada
Keyword(s):  
Resource Allocation ◽  
Container Terminal ◽  
Solution Space ◽  
Allocation Problem ◽  
Analysis Approach ◽  
Scheduling Problem ◽  
Resource Allocation Problem ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Berth Scheduling

Berth scheduling can be described as the resource allocation problem of berth space to vessels in a container terminal. When defining the allocation of berths to vessels container terminal operators set several objectives which ideally need to be optimized simultaneously. These multiple objectives are often non-commensurable and gaining an improvement on one objective often causes degrading performance on the other objectives. In this paper, the authors present the application of a multi-objective decision and analysis approach to the berth scheduling problem, a resource allocation problem at container terminals. The proposed approach allows the port operator to efficiently select a subset of solutions over the entire solution space of berth schedules when multiple and conflicting objectives are involved. Results from extensive computational examples using real-world data show that the proposed approach is able to construct and select efficient berth schedules, is consistent, and can be used with confidence.

The Soft Resource Allocation Problem

2009 ◽  
Author(s):  
Reza Ahmadi ◽  
Sriram Dasu ◽  
Foaad Iravani
Keyword(s):  
Resource Allocation ◽  
Allocation Problem ◽  
Resource Allocation Problem
Sign in / Sign up

Export Citation Format

Share Document